U.S. patent application number 15/410598 was filed with the patent office on 2018-07-19 for methods of learning long term brake corner specific torque variation.
This patent application is currently assigned to GM GLOBAL TECHNOLOGY OPERATIONS LLC. The applicant listed for this patent is GM GLOBAL TECHNOLOGY OPERATIONS LLC. Invention is credited to EDWARD T. HEIL, ALAN J. HOUTMAN, ERIC E. KRUEGER, PATRICK J. MONSERE, ROBERT L. NISONGER, BRANDON C. PENNALA, CONSTANDI J. SHAMI.
Application Number | 20180201243 15/410598 |
Document ID | / |
Family ID | 62716580 |
Filed Date | 2018-07-19 |
United States Patent
Application |
20180201243 |
Kind Code |
A1 |
PENNALA; BRANDON C. ; et
al. |
July 19, 2018 |
METHODS OF LEARNING LONG TERM BRAKE CORNER SPECIFIC TORQUE
VARIATION
Abstract
Systems and methods are provided for controlling a vehicle using
a specific torque of a brake system. In one embodiment, a method of
using a specific torque of a brake system for a vehicle includes:
determining a brake pressure of the brake system during a braking
operation; determining a deceleration of the vehicle during the
braking operation; determining a vehicle mass and a wheel radius;
estimating a specific torque of the brake system based on the brake
pressure and the deceleration; and operating the vehicle based on
the specific torque.
Inventors: |
PENNALA; BRANDON C.;
(HOWELL, MI) ; KRUEGER; ERIC E.; (CHELSEA, MI)
; MONSERE; PATRICK J.; (HIGHLAND, MI) ; HEIL;
EDWARD T.; (HOWELL, MI) ; NISONGER; ROBERT L.;
(MILFORD, MI) ; SHAMI; CONSTANDI J.; (ANN ARBOR,
MI) ; HOUTMAN; ALAN J.; (MILFORD, MI) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
GM GLOBAL TECHNOLOGY OPERATIONS LLC |
Detroit |
MI |
US |
|
|
Assignee: |
GM GLOBAL TECHNOLOGY OPERATIONS
LLC
Detroit
MI
|
Family ID: |
62716580 |
Appl. No.: |
15/410598 |
Filed: |
January 19, 2017 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
B60T 8/1701 20130101;
B60T 17/22 20130101; B60T 2270/86 20130101; B60T 8/18 20130101;
B60T 8/321 20130101; B60T 8/172 20130101; B60T 13/66 20130101 |
International
Class: |
B60T 8/32 20060101
B60T008/32; B60T 17/22 20060101 B60T017/22; B60T 8/172 20060101
B60T008/172; B60T 8/17 20060101 B60T008/17; B60T 8/18 20060101
B60T008/18 |
Claims
1. A method of using a specific torque of a brake system for a
vehicle, the method comprising: determining a brake pressure of the
brake system during a braking operation; determining a deceleration
of the vehicle during the braking operation; determining a vehicle
mass and a wheel radius of the vehicle; estimating a specific
torque of the brake system based on the brake pressure, the vehicle
mass, the wheel radius, and the deceleration; and operating the
vehicle based on the specific torque.
2. The method of claim 1, further comprising determining whether
the braking operation is a qualifying brake application that is
suitable for learning the specific torque, and wherein estimating
the specific torque is in response to determining that the braking
operation is the qualifying brake application.
3. The method of claim 2, wherein determining whether the braking
operation is a qualifying brake operation is based on a vehicle
mass, a road grade, a rain status, a road surface coefficient, a
brake burnish status, and a rate of change of the deceleration.
4. The method of claim 1, further comprising resetting the specific
torque to an initial specific torque value in response to receiving
a service reset request indicating a change of hardware in the
brake system.
5. The method of claim 1, wherein estimating the specific torque
includes estimating the specific torque as: Specific Torque =
Vehicle Mass * Deceleration * Tire Radius Brake Pressure
##EQU00002##
6. The method of claim 1, further comprising storing the specific
torque based on a brake temperature and an ambient humidity during
the braking operation.
7. The method of claim 6, wherein storing the specific torque
includes storing the specific torque in a three dimensional lookup
table.
8. The method of claim 6, wherein storing the specific torque
includes storing the specific torque as a deviation percent from an
initial specific torque value.
9. The method of claim 8, wherein storing the specific torque is in
response to determining that the specific torque is within a
threshold percent of the initial specific torque value.
10. The method of claim 1, further comprising: comparing the
specific torque to a fault threshold; and indicating a brake system
fault in response to the specific torque extending beyond the fault
threshold.
11. The method of claim 1, wherein the brake pressure is a
hydraulic brake pressure.
12. A vehicle system for controlling a vehicle with a brake system,
the vehicle system comprising: a sensor system configured for
determining a vehicle mass, a wheel radius, and a deceleration of
the vehicle during the braking operation; a brake pressure module
configured for determining a brake pressure of the brake system
during a braking operation; a torque estimation module for
estimating a specific torque of the brake system based on the
vehicle mass, the wheel radius, the brake pressure, and the
deceleration; and a brake system for operating the vehicle based on
the specific torque.
13. The vehicle system of claim 12, further comprising: a
qualifying brake apply module configured for determining whether
the braking operation is a qualifying brake application that is
suitable for learning the specific torque, and wherein the torque
estimation module is configured for estimating the specific torque
in response to determining that the braking operation is the
qualifying brake application.
14. The vehicle system of claim 13, wherein the qualifying brake
apply module is configured for determining whether the braking
operation is a qualifying brake operation based on a vehicle mass,
a road grade, a rain status, a road surface coefficient, a brake
burnish status, and a rate of change of the deceleration.
15. The vehicle system of claim 12, further comprising a service
reset module for resetting the specific torque to an initial
specific torque value in response to receiving a service reset
request indicating a change of hardware in the brake system.
16. The vehicle system of claim 12, wherein the torque estimation
module is configured for estimating the specific torque as:
Specific Torque = Vehicle Mass * Deceleration * Tire Radius Brake
Pressure ##EQU00003##
17. The vehicle system of claim 12, wherein the torque estimation
module is configured for storing the specific torque based on a
brake temperature and an ambient humidity during the braking
operation.
18. The vehicle system of claim 17, wherein storing the specific
torque is in response to determining that the specific torque is
within a threshold percent of the initial specific torque
value.
19. The vehicle system of claim 12, wherein the torque estimation
module is configured for storing the specific torque as a deviation
percent from an initial specific torque value.
20. A vehicle, comprising: a sensor system configured for
determining a vehicle mass, a wheel radius, and a deceleration of
the vehicle during the braking operation; a control system
comprising: a brake pressure module configured for determining a
brake pressure of the brake system during a braking operation; a
torque estimation module for estimating a specific torque of the
brake system based on the vehicle mass, the wheel radius, the brake
pressure, and the deceleration; and a brake system for operating
the vehicle based on the specific torque.
Description
TECHNICAL FIELD
[0001] The present disclosure generally relates to autonomous
vehicles, and more particularly relates to systems and methods for
brake corner specific torque variation in an autonomous
vehicle.
INTRODUCTION
[0002] An autonomous vehicle is a vehicle that is capable of
sensing its environment and navigating with little or no user
input. An autonomous vehicle senses its environment using sensing
devices such as radar, lidar, image sensors, and the like. The
autonomous vehicle further uses information from global positioning
systems (GPS) technology, navigation systems, vehicle-to-vehicle
communication, vehicle-to-infrastructure technology, and/or
drive-by-wire systems to navigate the vehicle.
[0003] Vehicle automation has been categorized into numerical
levels ranging from Zero, corresponding to no automation with full
human control, to Five, corresponding to full automation with no
human control. Various automated driver-assistance systems, such as
cruise control, adaptive cruise control, and parking assistance
systems correspond to lower automation levels, while true
"driverless" vehicles correspond to higher automation levels.
[0004] Some of the vehicle automation relies on converting a brake
torque request (e.g., a requested deceleration rate or a requested
brake torque value) into a hydraulic brake pressure in the braking
system. The relationship between the actual brake torque and the
brake pressure is known as specific torque. The specific torque is
generally based on original equipment manufacturer (OEM) brake
hardware in a non-worn condition. The actual specific torque of a
system, however, may vary from the OEM brake hardware in a non-worn
condition. For example, aftermarket brake hardware may have a
specific torque that varies by more than 20% from the OEM brake
hardware. Furthermore, wear on brake pads and rotors and
environmental changes such as temperature and humidity may impact
the specific torque of the braking system.
[0005] Accordingly, it is desirable to provide systems and methods
that allow the brake control system to adapt to long term changes
in specific torque. Furthermore, other desirable features and
characteristics of the present invention will become apparent from
the subsequent detailed description and the appended claims, taken
in conjunction with the accompanying drawings and the foregoing
technical field and background.
SUMMARY
[0006] Systems and methods are provided for controlling a vehicle
using a specific torque of a brake system. In one embodiment, a
method of using a specific torque of a brake system for a vehicle
includes: determining a brake pressure of the brake system during a
braking operation; determining a deceleration of the vehicle during
the braking operation; determining a vehicle mass and a wheel
radius; estimating a specific torque of the brake system based on
the brake pressure, the vehicle mass, the wheel radius, and the
deceleration; and operating the vehicle based on the specific
torque.
[0007] In one embodiment, a vehicle system for controlling a
vehicle with a brake system includes a sensor system, a brake
pressure module, a torque estimation module, and a brake system.
The sensor system is configured for determining a vehicle mass, a
wheel radius, and a deceleration of the vehicle during the braking
operation. The brake pressure module is configured for determining
a brake pressure of the brake system during a braking operation.
The torque estimation module is for estimating a specific torque of
the brake system based on the brake pressure, the vehicle mass, the
wheel radius, and the deceleration. The brake system is configured
for operating the vehicle based on the specific torque.
[0008] In one embodiment, a vehicle includes a sensor system, a
control system, and a braking system. The sensor system is
configured for determining a vehicle mass, a wheel radius, and a
deceleration of the vehicle during the braking operation. The
control system includes a brake pressure module configured for
determining a brake pressure of the brake system during a braking
operation. The control system further includes a torque estimation
module for estimating a specific torque of the brake system based
on the brake pressure, the vehicle mass, the wheel radius, and the
deceleration. The brake system is configured for operating the
vehicle based on the specific torque.
BRIEF DESCRIPTION OF THE DRAWINGS
[0009] The exemplary embodiments will hereinafter be described in
conjunction with the following drawing figures, wherein like
numerals denote like elements, and wherein:
[0010] FIG. 1 is a functional block diagram illustrating an
autonomous vehicle having a control system, in accordance with
various embodiments;
[0011] FIG. 2 is a dataflow diagram illustrating a control system
of the autonomous vehicle, in accordance with various
embodiments;
[0012] FIG. 3 is a graph illustrating a specific torque adjustment
map, in accordance with various embodiments; and
[0013] FIGS. 4A and 4B combine to form FIG. 4, which is a flowchart
illustrating a control method for controlling the autonomous
vehicle, in accordance with various embodiments.
DETAILED DESCRIPTION
[0014] The following detailed description is merely exemplary in
nature and is not intended to limit the application and uses.
Furthermore, there is no intention to be bound by any expressed or
implied theory presented in the preceding technical field,
background, brief summary or the following detailed description. As
used herein, the term module refers to any hardware, software,
firmware, electronic control component, processing logic, and/or
processor device, individually or in any combination, including
without limitation: application specific integrated circuit (ASIC),
an electronic circuit, a processor (shared, dedicated, or group)
and memory that executes one or more software or firmware programs,
a combinational logic circuit, and/or other suitable components
that provide the described functionality.
[0015] Embodiments of the present disclosure may be described
herein in terms of functional and/or logical block components and
various processing steps. It should be appreciated that such block
components may be realized by any number of hardware, software,
and/or firmware components configured to perform the specified
functions. For example, an embodiment of the present disclosure may
employ various integrated circuit components, e.g., memory
elements, digital signal processing elements, logic elements,
look-up tables, or the like, which may carry out a variety of
functions under the control of one or more microprocessors or other
control devices. In addition, those skilled in the art will
appreciate that embodiments of the present disclosure may be
practiced in conjunction with any number of systems, and that the
systems described herein is merely exemplary embodiments of the
present disclosure.
[0016] For the sake of brevity, conventional techniques related to
signal processing, data transmission, signaling, control, and other
functional aspects of the systems (and the individual operating
components of the systems) may not be described in detail herein.
Furthermore, the connecting lines shown in the various figures
contained herein are intended to represent example functional
relationships and/or physical couplings between the various
elements. It should be noted that many alternative or additional
functional relationships or physical connections may be present in
an embodiment of the present disclosure.
[0017] With reference to FIG. 1, a control system shown generally
at 100 is associated with a vehicle 10 in accordance with various
embodiments. In general, control system 100 estimates and learns an
actual specific torque of vehicle 10 to provide consistent braking
performance when faced with long term wear and/or aftermarket brake
hardware.
[0018] As depicted in FIG. 1, the vehicle 10 generally includes a
chassis 12, a body 14, front wheels 16, and rear wheels 18. The
body 14 is arranged on the chassis 12 and substantially encloses
components of the vehicle 10. The body 14 and the chassis 12 may
jointly form a frame. The wheels 16-18 are each rotationally
coupled to the chassis 12 near a respective corner of the body
14.
[0019] In various embodiments, the vehicle 10 is an autonomous
vehicle and the control system 100 is incorporated into the vehicle
10. The vehicle 10 is, for example, a vehicle that is automatically
controlled to carry passengers from one location to another. The
vehicle 10 is depicted in the illustrated embodiment as a passenger
car, but it should be appreciated that any other vehicle including
motorcycles, trucks, sport utility vehicles (SUVs), recreational
vehicles (RVs), marine vessels, aircraft, etc., can also be used.
In an exemplary embodiment, the vehicle 10 is a so-called Level
Four or Level Five automation system. A Level Four system indicates
"high automation", referring to the driving mode-specific
performance by an automated driving system of all aspects of the
dynamic driving task, even if a human driver does not respond
appropriately to a request to intervene. A Level Five system
indicates "full automation", referring to the full-time performance
by an automated driving system of all aspects of the dynamic
driving task under all roadway and environmental conditions that
can be managed by a human driver.
[0020] As shown, the vehicle 10 generally includes a propulsion
system 20, a transmission system 22, a steering system 24, a brake
system 26, a sensor system 28, an actuator system 30, at least one
data storage device 32, at least one controller 34, and a
communication system 36. The propulsion system 20 may, in various
embodiments, include an internal combustion engine, an electric
machine such as a traction motor, and/or a fuel cell propulsion
system. The transmission system 22 is configured to transmit power
from the propulsion system 20 to the vehicle wheels 16-18 according
to selectable speed ratios. According to various embodiments, the
transmission system 22 may include a step-ratio automatic
transmission, a continuously-variable transmission, or other
appropriate transmission. The brake system 26 is configured to
provide braking torque to the vehicle wheels 16-18. The brake
system 26 may, in various embodiments, include friction brakes,
brake by wire, a regenerative braking system such as an electric
machine, and/or other appropriate braking systems. The steering
system 24 influences a position of the of the vehicle wheels 16-18.
While depicted as including a steering wheel for illustrative
purposes, in some embodiments contemplated within the scope of the
present disclosure, the steering system 24 may not include a
steering wheel.
[0021] The sensor system 28 includes one or more sensing devices
40a-40n that sense observable conditions of the exterior
environment and/or the interior environment of the vehicle 10. The
sensing devices 40a-40n can include, but are not limited to,
radars, lidars, global positioning systems, optical cameras,
thermal cameras, ultrasonic sensors, and/or other sensors. The
actuator system 30 includes one or more actuator devices 42a-42n
that control one or more vehicle features such as, but not limited
to, the propulsion system 20, the transmission system 22, the
steering system 24, and the brake system 26. In various
embodiments, the vehicle features can further include interior
and/or exterior vehicle features such as, but are not limited to,
doors, a trunk, and cabin features such as air, music, lighting,
etc. (not numbered).
[0022] The data storage device 32 stores data for use in
automatically controlling the vehicle 10. In various embodiments,
the data storage device 32 stores defined maps of the navigable
environment. In various embodiments, the defined maps may be
predefined by and obtained from a remote system (described in
further detail with regard to FIG. 2). For example, the defined
maps may be assembled by the remote system and communicated to the
vehicle 10 (wirelessly and/or in a wired manner) and stored in the
data storage device 32. As can be appreciated, the data storage
device 32 may be part of the controller 34, separate from the
controller 34, or part of the controller 34 and part of a separate
system.
[0023] The controller 34 includes at least one processor 44 and a
computer readable storage device or media 46. The processor 44 can
be any custom made or commercially available processor, a central
processing unit (CPU), a graphics processing unit (GPU), an
auxiliary processor among several processors associated with the
controller 34, a semiconductor based microprocessor (in the form of
a microchip or chip set), a macroprocessor, any combination
thereof, or generally any device for executing instructions. The
computer readable storage device or media 46 may include volatile
and nonvolatile storage in read-only memory (ROM), random-access
memory (RAM), and keep-alive memory (KAM), for example. KAM is a
persistent or non-volatile memory that may be used to store various
operating variables while the processor 44 is powered down. The
computer-readable storage device or media 46 may be implemented
using any of a number of known memory devices such as PROMs
(programmable read-only memory), EPROMs (electrically PROM),
EEPROMs (electrically erasable PROM), flash memory, or any other
electric, magnetic, optical, or combination memory devices capable
of storing data, some of which represent executable instructions,
used by the controller 34 in controlling the vehicle 10.
[0024] The instructions may include one or more separate programs,
each of which comprises an ordered listing of executable
instructions for implementing logical functions. The instructions,
when executed by the processor 44, receive and process signals from
the sensor system 28, perform logic, calculations, methods and/or
algorithms for automatically controlling the components of the
vehicle 10, and generate control signals to the actuator system 30
to automatically control the components of the vehicle 10 based on
the logic, calculations, methods, and/or algorithms. Although only
one controller 34 is shown in FIG. 1, embodiments of the vehicle 10
may include any number of controllers 34 that communicate over any
suitable communication medium or a combination of communication
mediums and that cooperate to process the sensor signals, perform
logic, calculations, methods, and/or algorithms, and generate
control signals to automatically control features of the vehicle
10.
[0025] In various embodiments, one or more instructions of the
controller 34 are embodied in the control system 100 and, when
executed by the processor 44, predict the road surface friction
coefficient .mu.. For example, the instructions may approximate
surface .mu. based on sensor input and real-time weather data to
adjust path planning, calculate safe stopping distances, predict
evasive maneuver capability, and change chassis controls systems
proactively.
[0026] The communication system 36 is configured to wirelessly
communicate information to and from other entities 48, such as but
not limited to, other vehicles ("V2V" communication),
infrastructure ("V2I" communication), remote systems, and/or
personal devices (described in more detail with regard to FIG. 2).
In an exemplary embodiment, the communication system 36 is a
wireless communication system configured to communicate via a
wireless local area network (WLAN) using IEEE 802.11 standards or
by using cellular data communication. However, additional or
alternate communication methods, such as a dedicated short-range
communications (DSRC) channel, are also considered within the scope
of the present disclosure. DSRC channels refer to one-way or
two-way short-range to medium-range wireless communication channels
specifically designed for automotive use and a corresponding set of
protocols and standards.
[0027] Referring now to FIG. 2, and with continued reference to
FIG. 1, a dataflow diagram illustrates various embodiments of the
control system 100, which may be embedded within the controller 34.
Various embodiments of the control system 100 according to the
present disclosure may include any number of sub-modules embedded
within the controller 34. As can be appreciated, the sub-modules
shown in FIG. 2 may be combined and/or further partitioned to
similarly control the vehicle 10. Inputs to the control system 100
may be received from the sensor system 28, received from other
control modules (not shown) associated with the vehicle 10,
received from the communication network 56 at the communication
system 36, and/or determined/modeled by other sub-modules (not
shown) within the controller 34. In various embodiments, the
control system 100 includes a qualifying brake apply module 205, a
torque estimation module 210, a threshold comparison module 215, a
current specific torque database 220, a fault indication module
225, a service reset module 235, an initial specific torque
database 240, a brake torque request module 250, and a brake
pressure module 255.
[0028] Generally, control system 100 is configured to reduce
performance variation of a braking system due to long term changes
in vehicle level specific torque. Specific torque is the
relationship between brake pressure and brake torque. Specific
torque changes in the braking system are gradually learned by
monitoring the brake pressure to vehicle deceleration relationship
under certain conditions. Accordingly, control system 100 is able
to control electrohydraulic brake systems to provide increased
torque accuracy during driver applied and autonomous braking
events.
[0029] Qualifying brake apply module 205 is configured to receive
vehicle condition data 305 from sensor system 28, to receive brake
torque request 350, and to generate brake apply qualification
determination 310. In the example provided, vehicle condition data
305 includes vehicle deceleration, a brake temperature estimate,
ambient humidity, a rain sensor or wiper status, a vehicle mass
estimate, a wheel radius, a road grade estimate, a surface friction
coefficient estimate, and a brake burnish status. Vehicle condition
data 305 may be measured directly or may be estimated based on
measurements. For example, vehicle deceleration may be estimated
based on wheel speed sensor data or may be measured with an
accelerometer. In some embodiments, the brake burnish status is
estimated based on the nature of brake torque requests since the
last brake hardware change. In the example provided, the wheel
effective radius estimate is an estimated effective wheel radius
based on a tire pressure measurement from a Tire Pressure
Monitoring System.
[0030] In some embodiments, the sensors used for autonomous driving
(e.g., LIDAR sensors, RADAR sensors, Global Navigation Satellite
System (GNSS) receivers, etc.) may be utilized for the estimates
and/or measurements. For example, the sensors may be used to count
the number of and measure the size of people and items entering and
exiting the vehicle. The vehicle may then estimate the mass of the
people and the items using a basic estimate of the density of the
people and the items. The mass of the people and items in the
vehicle may then be added to the mass of the vehicle when empty to
achieve a vehicle mass estimate. The sensors may similarly provide
accurate road grade information based on detecting the vehicle
location and matching the vehicle location to a known road map.
[0031] In some embodiments, qualifying brake apply module 205 is
configured to determine that a braking event is a qualifying brake
application when the current vehicle mass is nominal (e.g., not
overloaded), the road grade is substantially flat, the rotors are
not wet (e.g., wipers off, rain sensor does not detect water), the
road friction coefficient is high, the brakes are burnished, and
the brake torque request indicates a sustained constant
deceleration. In some embodiments, qualifying brake apply module
205 omits some of these considerations.
[0032] Torque estimation module 210 is configured to receive
vehicle condition data 305, to receive brake apply qualification
determination 310, and to generate estimated specific torque 315.
Torque estimation module 210 uses brake pressure and vehicle
deceleration feedback to estimate real-time specific torque. In the
example provided, torque estimation module calculates estimated
specific torque 315 at specific brake temperatures and ambient
humidity values to learn the brake system dependency on brake
temperature and ambient humidity, which can vary between different
brake pad and rotor combinations. As described below, estimated
specific torque 315 may be calculated to learn dependency on brake
pressure in addition to ambient humidity and brake temperature.
Accordingly, control system 100 provides an ability to "learn"
after-market brake hardware specific torque and brake
temperature/ambient humidity dependency.
[0033] In the example provided, torque estimation module 210
calculates estimated specific torque 315 according to the
equation:
Specific Torque = Vehicle Mass * Deceleration * Tire Radius Brake
Pressure ( eq . 1 ) ##EQU00001##
[0034] In some embodiments, deceleration refers to deceleration of
the vehicle due to the brake system. For example, when vehicle
condition data 305 provides a total vehicle deceleration relative
to the road, then torque estimation module 210 may modify the total
vehicle deceleration based on road grade information (e.g., add or
subtract acceleration due to gravity) to obtain the deceleration
due to the brake system. In some embodiments, a substantially
non-zero road grade may disqualify the braking operation from being
a qualifying brake apply, and the deceleration may be assumed to be
due to the brake system even when vehicle condition data 305
provides a total vehicle deceleration relative to the road.
[0035] Threshold comparison module 215 is configured to receive
vehicle condition data 305 and current specific torque 320.
Threshold comparison module 215 is configured to generate update
database indicator 325. Threshold comparison module 215 compares
estimated specific torque 315 to current specific torque 320. When
estimated specific torque 315 varies from current specific torque
320 by more than a threshold amount, threshold comparison module
215 generates update database indicator 325.
[0036] Current specific torque database 220 is configured to store
and generate current specific torque 320, to receive estimated
specific torque 315, to receive update database indicator 325, to
receive specific torque reset indicator 340, and to receive initial
specific torque value 345. Current specific torque database
replaces current specific torque 320 with estimated specific torque
315 in response to receiving update database indicator 325. Current
specific torque database 220 replaces current specific torque 320
with initial specific torque value 345 in response to receiving
specific torque reset indicator 340. In the example provided,
current specific torque database 220 is non-volatile random access
memory (NVRAM) that stores the current specific torque across key
cycles of the vehicle. The current specific torque may be stored as
specific torque values, as a deviation value or percent from the
initial specific torque value, or as any other indicator that may
be used to calculate the specific torque value.
[0037] Referring now to FIG. 3, and with continued reference to
FIGS. 1-2, a specific torque adjustment map 400 is illustrated in
accordance with various embodiments. In the example provided,
current specific torque 320 is stored in a three dimensional lookup
table as a percent variation 405 at specific brake temperatures 410
and ambient humidity values 415. For example, estimated specific
torque 315 may be stored as current specific torque 320 indicating
a -5% change from initial specific torque value 345 at a specified
brake temperature and ambient humidity.
[0038] In some embodiments, the amount of change to specific torque
adjustment map 400 from any single received estimated specific
torque 315 is limited to improve robustness and reduce the impact
of outlier estimations. In some embodiments, a learned difference
at a particular point on the map is used to adjust surrounding
points as well. For example, when estimated specific torque 315
indicates that current specific torque 320 should move from 0% to
-5% difference from initial specific torque value 345, surrounding
points 420 and 422 may be adjusted to the negative direction (e.g.,
to -2.5%) when the surrounding points 420 and 422 do not yet have
any supporting measurements. In some embodiments, the total amount
of allowed deviation between the initial specific torque and the
current specific torque is bounded (e.g., limited to 25%
deviation).
[0039] In the example provided, specific torque adjustment map 400
is learned gradually over the course of days or weeks of driving.
It should be appreciated that the rate of learning may be adjusted
in any particular implementation, and may be accelerated based on
receiving the specific torque reset indicator 340 without departing
from the scope of the present disclosure.
[0040] In some embodiments, the control system accounts for
nonlinear relationships between brake pressure and brake torque due
to offsets and varying gain with input pressure. For example, the
control system may create multiple specific torque adjustment maps
400, with each specific torque adjustment map 400 being applicable
to a specified range of brake pressures to account for
nonlinearities as a function of pressure in addition to as a
function of temperature and humidity as described above. It should
be appreciated that other methods of storing and looking up
specific torque data as a function of temperature, humidity, and
brake pressure may be utilized without departing from the scope of
the present disclosure.
[0041] Referring again to FIG. 2, and with continuing reference to
FIGS. 1 and 3, fault indication module 225 receives estimated
specific torque 315 and generates fault data 330. Fault indication
module 225 compares estimated specific torque 315 with threshold
values, such as government regulated minimum specific torque values
or specific torque values that may indicate faulty brake hardware.
Fault data 330 indicates that estimated specific torque 315 is
outside of the threshold values. A maintenance module 230 receives
fault data 330 for indicating to a driver/passenger or to
controller 34 that brake system maintenance should be performed.
Accordingly, fault indication module 225 may be used for continuous
monitoring of brake hardware performance and may alert the driver
and/or control system if performance degrades beyond a set
limit.
[0042] Service reset module 235 receives service reset request 335
and generates specific torque reset indicator 340. For example,
service reset request 335 may be entered by a technician who
changed brake pads and/or rotors of brake system 26. In some
embodiments, sensor system 28 may detect removal of brake pads
and/or rotors and service reset module 235 may generate service
reset request 335. Specific torque reset indicator 340 instructs
current specific torque database 220 to replace current specific
torque 320 with initial specific torque value 345.
[0043] Initial specific torque database 240 stores and generates
initial specific torque value 345. For example, initial specific
torque value 345 may indicate the specific torque for brake system
hardware installed by the manufacturer of vehicle 10.
[0044] Brake torque request module 250 generates brake torque
request 350. For example, brake torque request module 250 may
generate brake torque request 350 in response to controller 34
determining that a vehicle in front of vehicle 10 is decelerating.
In some embodiments, brake torque request module 250 indicates an
amount of deceleration to be achieved as a coefficient of the
acceleration due to gravity on Earth (G). In some embodiments,
brake torque request module 250 indicates a torque value to be
achieved by brake system 26. Brake pressure module 255 receives
current specific torque 320, receives brake torque request 350, and
generates brake pressure value 355 for brake system 26. Brake
torque request module 250 calculates brake pressure value 355
needed to achieve brake torque request 350 based on current
specific torque 320, as will be appreciated by those with ordinary
skill in the art. As used herein, brake pressure refers to the
hydraulic pressure within brake system 26. Brake pressure may be
known as corner pressure or wheel pressure.
[0045] Referring now to FIG. 4, and with continued reference to
FIGS. 1-3, a flowchart illustrates a control method 500 for using a
specific torque of a brake system for a vehicle that can be
performed by the control system 100 of FIG. 2 in accordance with
the present disclosure. As can be appreciated in light of the
disclosure, the order of operation within the method is not limited
to the sequential execution as illustrated in FIG. 4, but may be
performed in one or more varying orders as applicable and in
accordance with the present disclosure. In various embodiments, the
method 500 can be scheduled to run based on one or more
predetermined events, and/or can run continuously during operation
of the vehicle 10.
[0046] In general, method 500 is an algorithm that monitors a brake
pressure to vehicle deceleration relationship under certain
conditions to continually estimate the current brake pressure to
brake torque conversion factor (specific torque). This allows the
algorithm to gradually compensate for system wear and aftermarket
brake hardware. In the case of an autonomous vehicle, the algorithm
uses available inputs such as road grade and occupant/loading
estimates to determine when the vehicle is in a nominal condition
(e.g., level road and substantially lightly loaded vehicle weight)
appropriate for specific torque learning. The algorithm can also
use brake temperature and humidity inputs to learn the brake system
dependency on these factors, which may vary between different
pad/rotor combinations.
[0047] Control system 100 receives vehicle condition inputs in task
510. For example, qualifying brake apply module 205 and torque
estimation module 210 may receive vehicle condition data 305.
Vehicle condition data 305 indicates a brake pressure of the brake
system during a braking operation and a deceleration of the vehicle
during the braking operation.
[0048] Control system 100 determines whether a service reset is
indicated in task 515. For example, service reset module 235 may
generate specific torque reset indicator 340 in response to
receiving service reset request 335. When there is not a service
reset request, method 500 proceeds to task 525. When there is a
service reset request, method 500 proceeds to task 520.
[0049] Control system 100 resets the specific torque to an initial
specific torque value in response to receiving a service reset
request indicating a change of hardware in the brake system in task
520. For example, current specific torque database 220 may store
initial specific torque value 345 as current specific torque 320 in
response to receiving specific torque reset indicator 340.
[0050] Control system 100 analyzes a braking operation in task 525.
Control system 100 determines whether the braking operation is a
qualifying brake application that is suitable for learning the
specific torque in task 525. For example, qualifying brake apply
module 205 may generate brake apply qualification determination 310
in response to determining that the brake application is suitable
for learning the specific torque. In some embodiments, control
system 100 determines whether the braking operation is a qualifying
brake operation is based on a vehicle mass, a road grade, a rain
status, a road surface coefficient, a brake burnish status, and a
rate of change of the deceleration.
[0051] When the brake application is not a qualifying brake
application, method 500 ends. When the brake application is a
qualifying brake application, method 500 proceeds to task 535.
Control system 100 estimates a specific torque of the brake system
based on the brake pressure and the deceleration in task 535 in
response to determining that the braking operation is the
qualifying brake application. For example, specific torque
estimation module 210 may generate estimated specific torque
315.
[0052] Control system 100 compares the specific torque to an
initial specific torque value and stores the specific torque in
response to determining that the specific torque is outside of a
threshold percent of the initial specific torque value. For
example, threshold comparison module 215 may cause current specific
torque database 220 to replace current specific torque 320 with
estimated specific torque 315. In some embodiments, the control
system stores the specific torque as a deviation percent from an
initial specific torque value in a three dimensional lookup table
based on a brake temperature and an ambient humidity during the
braking operation.
[0053] Control system 100 compares the specific torque to a fault
threshold in task 545. For example, fault indication module 225 may
compare estimated specific torque 315 to a threshold. When the
specific torque is within the fault threshold, method 500 proceeds
to task 555. When the specific torque is outside of the fault
threshold, method 500 proceeds to task 550.
[0054] Control system 100 indicates a brake system fault in
response to the specific torque extending beyond the fault
threshold in task 550. For example, fault indication module 225 may
generate fault data 330.
[0055] Control system 100 operates the vehicle based on the
specific torque in task 555. For example, brake pressure module 255
may convert brake torque request 350 to brake pressure value 355
based on current specific torque 320.
[0056] Accordingly, the method may increase the accuracy of a feed
forward control term in autonomous driving systems featuring a
brake torque interface to the brake system. The method may further
provide consistent brake feel even when installing aftermarket
brake hardware (e.g. pads or rotors) results in a significant
change in specific torque. The method may further increase
consistency in autonomous braking performance in the presence of
system wear and/or aftermarket hardware.
[0057] While at least one exemplary embodiment has been presented
in the foregoing detailed description, it should be appreciated
that a vast number of variations exist. It should also be
appreciated that the exemplary embodiment or exemplary embodiments
are only examples, and are not intended to limit the scope,
applicability, or configuration of the disclosure in any way.
Rather, the foregoing detailed description will provide those
skilled in the art with a convenient road map for implementing the
exemplary embodiment or exemplary embodiments. It should be
understood that various changes can be made in the function and
arrangement of elements without departing from the scope of the
disclosure as set forth in the appended claims and the legal
equivalents thereof.
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